Near-surface Ozone Concentrations Research Articles

Large-scale ozone episodes in Europe: Decreasing sizes in the last decades but diverging changes in the future

Episodes of high near-surface ozone concentrations tend to cover large areas for several days. They are strongly dependent on meteorology, precursor emissions, and the ambient photochemical conditions. This study introduces a new pseudo-Lagrangian algorithm that identifies the spatiotemporal patterns of episodes, allowing for a good characterization of their areal extent and an assessment of their drivers. The algorithm has been used to identify ozone episodes in Europe from April to September over the last twenty years (2003–2022) in the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis as well as in the historical simulation (1950–2014) and four shared socio-economic pathways (SSPs, spanning 2015–2100) of three Earth system models (UKESM1-0-LL, EC-Earth3-AerChem and GFDL-ESM4). While the total number of episodes has increased in recent years, the frequency of large episodes has decreased following European precursor emission reductions. The analysis of the 100 largest episodes shows that they tend to occur in Northern Europe during spring and in the center and south of the continent from June onwards. Most of the top 10 episodes occurred in the first years of the century and were associated with high temperatures, enhanced solar radiation, and anticyclonic conditions. Despite the decrease in large episodes in recent years, there is uncertainty regarding future European episodes. Episodes of reduced size are found for SSPs with weak greenhouse forcing and low precursor emissions, whereas episode sizes increase in scenarios with high methane concentrations and enhanced radiative forcing, even exceeding the maximum historical size. However, the three models project episodes of different sizes for any given scenario, probably associated with their differing warming trends and the varying level of complexity in the implementation of processes. These results point to the need to implement both effective climate and air quality policies to address the ozone air pollution problem in Europe in a warming climate.

Estimation of Near-surface Ozone Concentration in the Beijing-Tianjin-Hebei Region Based on XGBoost-LME Model

High spatiotemporal resolution data on near-surface ozone concentration distribution is of great significance for monitoring and controlling atmospheric ozone pollution and improving the living environment. Using TROPOMI-L3 NO2, HCHO products, and ERA5-land high-resolution data as estimation variables, an XGBoost-LME model was constructed to estimate the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. The results showed that： ① Through correlation analysis, surface 2 m temperature （T2M）, 2 m dewpoint temperature （D2M）, surface solar radiation downwards （SSRD）, tropospheric formaldehyde （HCHO）, and tropospheric nitrogen dioxide （NO2） were important factors affecting the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. Among them, T2M, SSRD, and D2M had strong correlations, with correlation coefficients of 0.82, 0.75, and 0.71, respectively. ② Compared with that of other models, the XGBoost-LME model had the best performance in terms of various indicators. The ten-fold cross-validation evaluation indicators R2, MAE, and RMSE were 0.951, 9.27 μg·m-3, and 13.49 μg·m-3, respectively. At the same time, the model performed well at different time scales. ③ In terms of time, there was a significant seasonal difference in near-surface ozone concentration in the Beijing-Tianjin-Hebei Region in 2019, with the concentration changing in the order of summer > spring > autumn > winter. The monthly average ozone concentration in the region showed an inverted "V" trend, with a slight increase in September. The highest value occurred in July, whereas the lowest value occurred in December. In terms of spatial distribution, the near-surface ozone concentrations in the Beijing-Tianjin-Hebei Region during the months of February and March were generally at the same levels. In January, November, and December, there was a relatively insignificant trend of higher concentrations in the north and lower concentrations in the south. For the remaining months, the spatial distribution of near-surface ozone concentrations in this area predominantly exhibited a pattern of higher concentrations in the south and lower concentrations in the north. High-value areas were predominantly found in the plain regions of the southern part with lower altitudes, dense population, and higher industrial emissions； low-value areas, on the other hand, were primarily located in mountainous areas of the northern part with higher altitudes, sparse population, higher vegetation coverage, and lower industrial emissions.

WRF-Chem Modeling of Tropospheric Ozone in the Coastal Cities of the Gulf of Finland

Ozone in the troposphere is a pollutant and greenhouse gas. Atmospheric models can add valuable information to observations for studying the spatial and temporal variations in tropospheric ozone content. The present study is intended to evaluate the variability in tropospheric ozone and its precursors near the Gulf of Finland with a focus on St. Petersburg (Russia) and Helsinki (Finland) in 2016–2019, using the WRF-Chem 3-D numerical model with a spatial resolution of 10 km, together with observations. The diurnal cycle of the near-surface ozone concentrations (NSOCs) in both cities is caused by the variability in NO2 emissions, planetary boundary layer height, and local meteorological conditions. The seasonal variations in NSOCs and tropospheric ozone content (TrOC) are caused by the variability in total ozone content and in ozone formation in the troposphere. The model reveals a VOC-limited regime in the ~0–1 km layer around St. Petersburg, Helsinki, and the Gulf of Finland and a pronounced NOx-limited regime in the 0–2 km layer in the forests of southern Finland, Karelia, some Russian regions, and the Baltic countries in July. The WRF-Chem model overestimates the measured NSOCs by 10.7–43.5% and the TrOC by 7–10.4%. The observed differences are mainly caused by the errors in chemical boundary conditions and emissions of ozone precursors and by the coarse spatial resolution of the modeling.

A meta-analysis of elevated O3 effects on herbaceous plants antioxidant oxidase activity.

Increases in near-surface ozone (O3) concentrations is a global environmental problem. High-concentration O3 induces stress in plants, which can lead to visible damage to plants, reduced photosynthesis, accelerated aging, inhibited growth, and can even plant death. However, its impact has not been comprehensively evaluated because of the response differences between individual plant species, environmental O3 concentration, and duration of O3 stress in plants. We used a meta-analysis approach based on 31 studies 343 observations) to examine the effects of elevated O3 on malondialdehyde (MDA), superoxide dismutase (SOD), and peroxidase (POD) activities in herbaceous plants. Globally, important as they constitute the majority of the world's food crops. We partitioned the variation in effect size found in the meta-analysis according to the presence of plant species (ornamental herb, rice, and wheat), O3 concentration, and duration of O3 stress in plants. Our results showed that the effects of elevated O3 on plant membrane lipid peroxidation depending on plant species, O3 concentration, and duration of O3 stress in plants. The wheat SOD and POD activity was significantly lower compared to the herbs and rice (P<0.01). The SOD activity of all herbaceous plants increased by 34.6%, 10.5%, and 26.3% for exposure times to elevated O3 environments of 1-12, 13-30, and 31-60 days, respectively. When the exposure time was more than 60 days, SOD activity did not increase but significantly decreased by 12.1%. However, the POD activity of herbaceous plants increased by 30.4%, 57.3%, 21.9% and 5.81%, respectively, when exposure time of herbaceous plants in elevated O3 environment was 1-12, 13-30, 31-60 and more than 60 days. Our meta-analysis revealed that (1) rice is more resistant to elevated O3 than wheat and ornamental herbs likely because of the higher activity of antioxidant components (e.g., POD) in the symplasts, (2) exposure to elevated O3 concentrations for >60 days, may result in antioxidant SOD lose its regulatory ability, and the antioxidant component POD in the symplast is mainly used to resist O3 damage, and (3) the important factors affected the activity of SOD and POD in plants were not consistent: the duration of O3 stress in plants was more important than plant species and O3 concentration for SOD activity. However, for POD activity, plant species was the most important factor.

High-resolution estimation of near-surface ozone concentration and population exposure risk in China.

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China's near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China's eastern region, with population exposure risks mostly ranging from 0.8 to 5.

Impacts of projected changes in sea surface temperature on ozone pollution in China toward carbon neutrality

The global sea surface temperatures (SSTs) are expected to change diversely in the future under different climate scenarios, which will affect the near-surface ozone (O3) distribution and concentration by influencing meteorological states and large-scale atmospheric circulation. Many countries have planned to reach carbon neutrality by the mid-21st century. In this study, the impacts of global and regional SST changes on near-surface O3 concentrations in China in the middle of the 21st century under the carbon-neutral scenario (Shared Socioeconomic Pathway 1–1.9), compared with the high-emission scenario (Shared Socioeconomic Pathway 5–8.5), and possible physical and chemical mechanisms are investigated using the Community Earth System Model version 1 (CESM1). Under future climate change, the changes in SSTs in the carbon-neutral scenario relative to the high-emission scenario lead to a dipole change in near-surface O3 concentrations in eastern and western China, with a significant decrease of 0.79 ppbv in the eastern China and a significant increase of 1.05 ppbv in the western China. The cooling of North Pacific Ocean under the carbon-neutral scenario causes a decrease in near-surface O3 concentrations by 0.48 ppbv in eastern China due to the weakened chemical production and an increase by 0.74 ppbv in western China attributed to the enhanced O3 transport from Eurasia. Cooling of Southern Hemisphere oceans leads to anomalous upward air motions over eastern China, which weaken the vertical transport of high-elevation O3 to the surface, resulting in a reduction in near-surface O3 concentrations by 0.58 ppbv in eastern China. Our results suggest that future changes in SSTs in the carbon-neutral scenario will positively benefit O3 air quality improvement in the polluted eastern China, with the North Pacific and Southern Hemisphere oceans playing important roles.

Shifting summer holidays in Spain as an adaptation measure to climate change

This paper assesses whether moving summer holidays to the warmest period of the year in Spain could be a useful climate change adaptation strategy. While the most popular period for Spanish summer holidays has traditionally been August, we illustrate that the second half of July is the hottest period of the year and when the negative effects of high temperatures are most pronounced. If the holiday period in the second fortnight of August was moved to the second fortnight of July, some of the associated impacts would be mitigated due to the reduced anthropogenic activity during non-working days. In particular, we find a significant reduction in the annual peak of labour productivity loss (~25 %) and, to a lesser extent, of electricity demand and near-surface ozone concentrations (~3–4 %). Finally, we also show that global warming could lead to enhanced differences between both fortnights (even with no change in the seasonal cycle of temperature) because of the non-linear relationships between temperature and its impacts. Therefore, the positive effect of shifting holidays would be even larger in the coming future.

Rebuilding high-quality near-surface ozone data based on the combination of WRF-Chem model with a machine learning method to better estimate its impact on crop yields in the Beijing-Tianjin-Hebei region from 2014 to 2019

In recent years, the problem of surface ozone pollution in China has been of great concern. According to observation data from monitoring stations, the concentration of near-surface ozone (O3) in China has gradually increased in recent years, and ozone concentration often exceeds the contaminant limit standard, especially in the Beijing-Tianjin-Hebei (BTH) region. High O3 concentration pollution will adversely affect crop growth, which can cause crop yield losses. Therefore, it is urgent to recognize the situation of ozone pollution in the BTH region and quantitatively evaluate the crop yield losses caused by ozone pollution to develop more effective pollution prevention and control policies. However, the monitoring of ozone concentration in China started relatively late compared with some developed countries, and currently, long-time series data covering the BTH region cannot be obtained, which makes it difficult to evaluate the impact of ozone on crop yield. Therefore, a new method (WRFC-XGB) was proposed in this study to establish a high-precision near-surface O3 concentration dataset covering the whole BTH region from 2014 to 2019 by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) machine learning algorithm. Through verification with ground observation station data, the results of WRFC-XGB are satisfactory, and R2 can reach 0.78–0.91. Compared with other algorithms, the accuracy of the near-surface ozone concentration dataset is greatly improved, which can be used to estimate the impact of surface ozone on crop yield. Based on this dataset, the yield loss of winter wheat, rice, and maize caused by O3 pollution was estimated by using the response equation of the relative yield and ozone dose index. The results showed that the total yield losses of winter wheat, rice and maize from 2014 to 2019 were 2659.21 million tons, 49.23 million tons and 1721.56 million tons due to ozone pollution in the BTH region, respectively, and the highest relative yield loss of crops caused by O3 pollution could be 29.37% during 2014–2019, which indicated that the impact of ozone pollution on crop yield cannot be ignored, and effective measures need to be developed to control ozone pollution, prevent crop production loss, and ensure people's food security.

Spatiotemporal dynamics of near-surface ozone concentration and potential source areas in northern China during 2015-2020.

Near-surface ozone (O3) pollution has become one of the main factors hampering urban air quality in northern China. However, on a spatiotemporal scale, dynamic transport paths and potential source areas of O3 in northern China are ambiguous. In addition, we suspect that the contribution of transportation activities to urban O3 concentrations developed in northern China may be underestimated. In this study, the HYSPLIT, PSCF, CWT and GTWR model were used to study the transmission paths, potential source areas and driving factors of urban O3 concentration on a spatiotemporal scale. The average annual concentration of surface O3 (the 90th percentile of MDA8) was 172 ± 29μg/m3 in northern China from 2015 to 2020. In terms of inter-annual variation, the urban O3 concentration increased from 2015 to 2018, and decreased after 2018. On the spatial scale, the areas with high O3 concentration were mainly clustered in industrial cities (Tangshan, Baoding, Shijiazhuang, Xingtai and Handan). During the study period, the area with high O3 concentration in northern China shifted from northwest to southeast. From 2015 to 2020, the influence of long-distance air mass trajectories from Xinjiang and Siberi on airflow transport in Beijing city dominates (78.60%) The average percentage of short-distance transport trajectories from Shandong Peninsula region is about 21.40%. The core potential source areas of O3 pollution shifted from northwest to southeast, but the contribution to O3 pollution in Beijing gradually weakened during the same period. Temperature and relative humidity were the main meteorological driving factors affecting O3 concentration in the study area, while population density, the proportion of secondary industry in GDP, industrial smoke (dust) emissions, and passenger traffic were the main non-meteorological factors. During the period study, the influence of industrial and traffic emissions had a more significant impact on O3 concentration in northern China, which will require that more attention be paid to emission mitigation in the regional industrial and passenger transportation sector, as well as the joint prevention and control of O3 pollution in northern China in the future.

Relationship between summer time near-surface ozone concentration and planetary boundary layer height in Beijing

The planetary boundary layer height (PBLH) is critical in affecting the concentration of air pollutants at surface. The relationship between PBLH and surface ozone concentration can be complicated but not entirely clear. In this study, by using observations and WRF-Chem simulations, we explore the relationship between the surface ozone and the PBLH in the summer daytime in Beijing, a major global ozone pollution hotspot. It is found that the surface ozone-PBLH relationship exhibits a three-stage evolution with ozone: first, increasing with PBLH, then keeping steady, and finally falling. The two turning points are around 1000 and 2000 m respectively. This process is likely controlled by the different contributions of chemical production and vertical mixing. Specifically, when PBLH is below ∼1000 m, as PBLH increases, chemical production increases owing to the light enhancement whereas the ozone contribution from vertical mixing remains weak due to the decreased ozone concentration gradient between the surface and the upper layer, resulting in a net gain of surface ozone concentration. When the PBL continues to rise, chemical production contributes less mainly due to the decrease of ozone precursors at the surface, whereas vertical mixing becomes more vigorous that further diluting surface ozone, resulting in a net loss of surface ozone concentration, especially when PBLH reaches above ∼2000 m. Our results contribute to understanding the variability of surface ozone in Beijing area and provide insights for local ozone air quality control.

Contrasting Near-Surface Ozone Pollution in Wet and Dry Year over China.

The near-surface ozone concentration was evaluated in two typical years with contrasting climatic impacts over the China region induced by El Niño-Southern Oscillation, which had either dry conditions (drought) with intense solar radiation and higher temperatures or wet conditions with opposite meteorological conditions. Surface ozone was observed to aggravate notably by 30% over Northern China in summer and by 50% over Eastern China in autumn in the dry year compared to the wet year. The ozone aggravation was found to be mainly ascribed to the reduced precipitation (relative humidity), enhanced solar radiation and increased temperature rather than primary emission (indicated by carbon monoxide). The health impacts showed the mortality attributable to ozone sharply increased by ~55% in Guangdong while the number of cases dying from ozone-related respiratory diseases per 100,000 population at risk was elevated by ~41% and ~17% for Guangdong (in the Pearl River Delta) and Jiangsu (in the Yangtze River Delta) province (two regions that have been reported to be highly influenced by surface ozone in China), respectively, in the dry year relative to the wet year, indicative of the significant adverse health effects of ozone aggravation. These results highlight the essential contribution of climate anomalies to surface ozone pollution. Efforts to suppress ozone aggravation can be beneficial to public health if extreme drought is predicted, and reasonable policy is implemented.

基于TROPOMI的近地面臭氧浓度估算研究

基于TROPOMI的近地面臭氧浓度估算研究

Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data

Ozone (O3) is an important greenhouse gas in the atmosphere. Stratospheric ozone protects human beings, but high near-surface ozone concentrations threaten environment and human health. Owing to the uneven distribution of ground-monitoring stations and the low time resolution of polar orbiting satellites, it is difficult to accurately evaluate the refinement and synergistic pollution of near-surface ozone in China. Besides, atmospheric circulation patterns also affect ozone concentrations greatly. In this study, a new generation of geostationary satellite is used to estimate the hourly near-surface ozone concentration with a spatial resolution of 0.05°. First, the Pearson correlation coefficient and maximum information coefficient were used to study the correlation between the top of atmospheric radiation (TOAR) of Himawari-8 satellite and O3 concentration; seven TOAR channels were selected. Second, based on an interpretable deep learning model, the hourly ozone concentration in China from September 2015 to August 2021 was obtained using the TOAR-O3 model. Finally, the self-organizing map method was used to determine six major summer weather circulation patterns in China. The results showed that (1) the near-surface O3 concentration can be accurately estimated; the R2 (RMSE: μg/m3) values of the daily, monthly, and annual tenfold cross validation results were 0.91 (12.74), 0.97 (5.64), and 0.98 (1.75), respectively. The feature importance of the model showed that the temperature, TOAR, and boundary layer height contributed 38 %, 22 %, and 13 %, respectively. (2) The O3 concentration showed obvious spatiotemporal difference and gradually increased from 10:00 to 15:00 (Beijing time) every day. In most areas of China, O3 concentration had increased significantly. (3) The O3 concentration in northern China was the highest under the circulation pattern of the Meiyu front over the Yangtze River Delta, while in southern China, it was the highest under the circulation pattern of the northeast cold vortex controlling most of China.

Impacts of deep boundary layer on near-surface ozone concentration over the Tibetan Plateau

The deep atmospheric boundary layer in winter and spring over the Tibetan Plateau (TP) facilitates the exchange of mass and chemical species between the troposphere and stratosphere. Using ERA5 reanalysis data (1979–2018), this paper analyzes the atmospheric process over the TP when the dynamical tropopause (1.5 PVU surface) approaches the top of the deep boundary layer, which would affect the concentration of near-surface ozone. The result shows that the near-surface (at the height about 500–550 hPa) ozone concentration increases when the tropopause descends and adjoins the top of the deep boundary layer. The influence mechanism of the deep boundary layer on the increase of surface ozone on February 27, 2008 is studied by the WRF-Chem model. It is found that the calculated (using Reynolds average method) near-surface ozone increases by about 8 ppb per hour due to the vertical transport when the ozone-rich air from the stratosphere penetrates into the troposphere. Furthermore, a large eddy simulation (LEM model) is used to investigate the turbulence structure and development during the formation of the deep atmospheric boundary layer in the same case. It is found that a separate weak-turbulence layer forms at the height of about 6000 m AGL at 1300 LT below the tropopause fold. The upper-level turbulence couples with the boundary-layer turbulence after 1900 LT. As a result, the strong boundary layer turbulence causes the ozone-rich air from the stratosphere to mix with air near the surface layer.

Ozone concentration at various heights near the surface layer in Shenyang, Northeast China

Ozone pollution has been growing in the recent decade, becoming a critical urban environmental issue in China. However, Shenyang’s near-surface ozone concentration (ground to 100 m altitude) is of grave concern. To obtain the ozone concentration and the corresponding meteorological conditions from 1 October 2018, to 30 September 2019, we built observatory sites at low (≈15 m) and high (≈90 m) layers in Shenyang, northeast China. Then, we analyzed the temporal variation of ozone concentration and discussed the relationship between ozone concentration and the meteorological factors at low and high layers. Compared with the high layer, the ozone concentration in the low layer is chartered with an earlier peak and higher diurnal range. The ozone concentration in all seasons first peaked at a low-layer height, in early in winter (15:00) and late summer (17:00). Then, the peak at high-layer height lagged for behind 1–2 h, generally occurring between 17:00 and 19:00. The variations in ozone concentration at low and high layers differed in daytime and nighttime. The diurnal ranges of ozone concentrations at the low layer were higher than those at the high layer in the four seasons examined. Overall, ozone concentration correlated significantly with air temperature, having correlation coefficients of 0.814 (p &amp;lt; 0.05) in the low layer and 0.731 (p &amp;lt; 0.05) in the high layer. Inversely, it showed a weak correlation with relative humidity: 0.310 (p &amp;lt; 0.05) and 0.351 (p &amp;lt; 0.05) in the low and high layers, respectively. Also, the correlation coefficients with wind speed were meager at both layers. Generally, ozone pollution is not severe in Shenyang.

Understanding Ozone Transport and Deposition within Indoor Surface Boundary Layers

Ozone-initiated oxidation reactions on indoor surfaces meaningfully alter the chemical composition of indoor air and human exposure to air toxins. Ozone mass transport within the indoor surface boundary layer plays a key role in ozone-surface reaction kinetics. However, limited information is available on detailed ozone transport dynamics near realistic, irregular indoor surfaces. This paper presents a research framework to study the underlying mechanisms of ozone reactions with realistic indoor surfaces based on microscope scanning of surface material and detailed Computational Fluid Dynamics (CFD) simulation. The study results show that indoor surface topography can meaningfully affect ozone mass transport within a surface boundary layer, thereby modulating near-surface ozone concentration gradient and surface uptake. The results also reveal that the effective indoor surface area available for ozone reaction varies with indoor air speed and turbulent air mixing within the boundary layer. The detailed dynamic behaviors of ozone reactions with realistic indoor surfaces provide insights into the implications of pollutant-surface interactions on indoor chemistry and air quality.

Estimation of the Near-Surface Ozone Concentration with Full Spatiotemporal Coverage across the Beijing-Tianjin-Hebei Region Based on Extreme Gradient Boosting Combined with a WRF-Chem Model

With the intensification of global warming and economic development in China, the near-surface ozone (O3) concentration has been increasing recently, especially in the Beijing-Tianjin-Hebei (BTH) region, which is the political and economic center of China. However, O3 has been measured in real time only over the past few years, and the observational records are discontinuous. Therefore, we propose a new method (WRFC-XGB) to establish a near-surface O3 concentration dataset in the BTH region by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) algorithm. Based on this method, the 8-h maximum daily average (MDA8) O3 concentrations are obtained with full spatiotemporal coverage at a spatial resolution of 0.1° × 0.1° across the BTH region in 2018. Two evaluation methods, sample- and station-based 10-fold cross-validation (10-CV), are used to assess our method. The sample-based (station-based) 10-CV evaluation results indicate that WRFC-XGB can achieve excellent accuracy with a high coefficient of determination (R2) of 0.95 (0.91), low root mean square error (RMSE) of 13.50 (17.70) µg m−3, and mean absolute error (MAE) of 9.60 (12.89) µg m−3. In addition, superb spatiotemporal consistencies are confirmed for this model, including the estimation of high O3 concentrations, and our WRFC-XGB model outperforms traditional models and previous studies in data mining. In addition, the proposed model can be applied to estimate the O3 concentration when it has not been measured. Furthermore, the spatial distribution analysis of the MDA8 O3 in 2018 reveals that O3 pollution in the BTH region exhibits significant seasonality. Heavy O3 pollution episodes mainly occur in summer, and the high O3 loading is distributed mainly in the southern BTH areas, which will pose challenges to atmospheric environmental governance for local governments.

Impacts of strong El Niño on summertime near-surface ozone over China

The influences of strong El Niño events (1997/98 and 2015/16) on summertime near-surface ozone (O3) concentrations over China are investigated using the GEOS-Chem model. The results show that near-surface O3 concentrations increased by a maximum of 6 ppb (parts per billion) during the summer of the developing phase of the 1997/98 El Niño in northeastern China, mainly due to the increased chemical production related to the hot and dry conditions. Besides, the O3 concentration increased by 3 ppb during the developing summer of both the 1997/98 and 2015/16 El Niño in southern China. It was linked to the weakened prevailing monsoon winds, which led to the accumulation of O3 in southern China. In contrast, in the summer of the decaying phase of the two El Niño events, O3 concentrations decreased over many regions of China when the El Niño reversed to the cooling phase. This highlights that El Niño plays an important role in modulating near-surface O3 concentrations over China.摘要利用全球大气化学三维模式 (GEOS-Chem) 模拟研究两次强厄尔尼诺事件 (1997/98和2015/16) 对中国夏季近地面臭氧 (O3) 浓度的影响. 结果表明1997/98年厄尔尼诺事件发展期夏季中国东北区域O3浓度升高, 最大值超过6ppb, 这主要归因于高温晴朗低湿等气象因素导致O3化学生成升高. 此外, 两次厄尔尼诺事件发展期夏季O3浓度在中国南部均增加了3ppb, 这与盛行季风减弱导致中国南方O3局地积累有关. 相反, 在两次强厄尔尼诺衰减期夏季, 中国大部分地区O3浓度下降伴随着海温模态转变为拉尼娜事件. 这表明厄尔尼诺在调节中国近地面O3浓度中发挥着重要作用.

Estimation of Near-Surface Ozone Concentration and Analysis of Main Weather Situation in China Based on Machine Learning Model and Himawari-8 Toar Data

Estimation of Near-Surface Ozone Concentration and Analysis of Main Weather Situation in China Based on Machine Learning Model and Himawari-8 Toar Data

Impacts of the COVID-19 economic slowdown on ozone pollution in the U.S.

In this work, we use observations and experimental emissions in a version of NOAA's National Air Quality Forecasting Capability to show that the COVID-19 economic slowdown led to disproportionate impacts on near-surface ozone concentrations across the contiguous U.S. (CONUS). The data-fusion methodology used here includes both U.S. EPA Air Quality System ground and the NASA Aura satellite Ozone Monitoring Instrument (OMI) NO2 observations to infer the representative emissions changes due to the COVID-19 economic slowdown in the U.S. Results show that there were widespread decreases in anthropogenic (e.g., NOx) emissions in the U.S. during March–June 2020, which led to widespread decreases in ozone concentrations in the rural regions that are NOx-limited, but also some localized increases near urban centers that are VOC-limited. Later in June–September, there were smaller decreases, and potentially some relative increases in NOx emissions for many areas of the U.S. (e.g., south-southeast) that led to more extensive increases in ozone concentrations that are partly in agreement with observations. The widespread NOx emissions changes also alters the O3 photochemical formation regimes, most notably the NOx emissions decreases in March–April, which can enhance (mitigate) the NOx-limited (VOC-limited) regimes in different regions of CONUS. The average of all AirNow hourly O3 changes for 2020–2019 range from about +1 to −4 ppb during March–September, and are associated with predominantly urban monitoring sites that demonstrate considerable spatiotemporal variability for the 2020 ozone changes compared to the previous five years individually (2015–2019). The simulated maximum values of the average O3 changes for March–September range from about +8 to −4 ppb (or +40 to −10%). Results of this work have implications for the use of widespread controls of anthropogenic emissions, particularly those from mobile sources, used to curb ozone pollution under the current meteorological and climate conditions in the U.S.